Misidentification and contamination of biobank samples (e.g. https://www.selleckchem.com/products/pf-06826647.html cell lines) have plagued biomedical research. Short tandem repeat (STR) and single-nucleotide polymorphism assays are widely used to authenticate biosamples and detect contamination, but with insufficient sensitivity at 5-10% and 3-5%, respectively. Here, we describe a deep NGS-based method with significantly higher sensitivity (?1%). It can be used to authenticate human and mouse cell lines, xenografts and organoids. It can also reliably identify and quantify contamination of human cell line samples, contaminated with only small amount of other cell samples; detect and quantify species-specific components in human-mouse mixed samples (e.g. xenografts) with 0.1% sensitivity; detect mycoplasma contamination; and infer population structure and gender of human samples. By adopting DNA barcoding technology, we are able to profile 100-200 samples in a single run at per-sample cost comparable to conventional STR assays, providing a truly high-throughput and low-cost assay for building and maintaining high-quality biobanks.Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single-cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm (Internal Spike-in-like-genes normalization) improves the performance of downstream analyses.The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.RNA conformational alteration has significant impacts on cellular processes and phenotypic variations. An emerging genetic factor of RNA conformational alteration is a new class of single nucleotide variant (SNV) named riboSNitch. RiboSNitches have been demonstrated to be involved in many genetic diseases. However, identifying riboSNitches is notably difficult as the signals of RNA structural disruption are often subtle. Here, we introduce a novel computational framework-RIboSNitch Predictor based on Robust Analysis of Pairing probabilities (Riprap). Riprap identifies structurally disrupted regions around any given SNVs based on robust analysis of local structural configurations between wild-type and mutant RNA sequences. Compared to previous approaches, Riprap shows higher accuracy when assessed on hundreds of known riboSNitches captured by various experimental RNA structure probing methods including the parallel analysis of RNA structure (PARS) and the selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE). Further, Riprap detects the experimentally validated riboSNitch that regulates human catechol-O-methyltransferase haplotypes and outputs structurally disrupted regions precisely at base resolution. Riprap provides a new approach to interpreting disease-related genetic variants. In addition, we construct a database (RiboSNitchDB) that includes the annotation and visualization of all presented riboSNitches in this study as well as 24 629 predicted riboSNitches from human expression quantitative trait loci.Generation of new genetic diversity by crossover (CO) and non-crossover (NCO) is a fundamental process in eukaryotes. Fungi have played critical roles in studying this process because they permit tetrad analysis, which has been used by geneticists for several decades to determine meiotic recombination products. New genetic variations can also be generated in zygotes via illegitimate mutation (IM) and repeat-induced point mutation (RIP). RIP is a genome defense mechanism for preventing harmful expansion of transposable elements or duplicated sequences in filamentous fungi. Although the exact mechanism of RIP is unknown, the CG to TA mutations might result from DNA cytosine methylation. A comprehensive approach for understanding the molecular mechanisms underlying these important processes is to perform high-throughput mapping of CO, NCO, RIP and IM in zygotes bearing large numbers of heterozygous variant markers. To this aim, we developed 'TSETA', a versatile and user-friendly pipeline that utilizes high-quality and chromosome-level genome sequences involved in a single meiotic event of the industrial workhorse fungus Trichoderma reesei. TSETA not only can be applied to most sexual eukaryotes for genome-wide tetrad analysis, it also outcompetes most currently used methods for calling out single nucleotide polymorphisms between two or more intraspecies strains or isolates.